VoPo leverages cellular heterogeneity for predictive modeling of single-cell data

Natalie Stanley, Ina A. Stelzer, Amy S. Tsai, Ramin Fallahzadeh, Edward Ganio, Martin Becker, Thanaphong Phongpreecha, Huda Nassar, Sajjad Ghaemi, Ivana Maric, Anthony Culos, Alan L. Chang, Maria Xenochristou, Xiaoyuan Han, Camilo Espinosa, Kristen Rumer, Laura Peterson, Franck Verdonk, Dyani Gaudilliere, Eileen Tsai, Dorien Feyaerts, Jakob Einhaus, Kazuo Ando, Ronald J. Wong, Gerlinde Obermoser, Gary M. Shaw, David K. Stevenson, Martin S. Angst, Brice Gaudilliere and Nima Aghaeepour ()
Additional contact information
Natalie Stanley: Stanford University
Ina A. Stelzer: Stanford University
Amy S. Tsai: Stanford University
Ramin Fallahzadeh: Stanford University
Edward Ganio: Stanford University
Martin Becker: Stanford University
Thanaphong Phongpreecha: Stanford University
Huda Nassar: Stanford University
Sajjad Ghaemi: Stanford University
Ivana Maric: Stanford University
Anthony Culos: Stanford University
Alan L. Chang: Stanford University
Maria Xenochristou: Stanford University
Xiaoyuan Han: Stanford University
Camilo Espinosa: Stanford University
Kristen Rumer: Stanford University
Laura Peterson: Stanford University
Franck Verdonk: Stanford University
Dyani Gaudilliere: Stanford University
Eileen Tsai: Stanford University
Dorien Feyaerts: Stanford University
Jakob Einhaus: Stanford University
Kazuo Ando: Stanford University
Ronald J. Wong: Stanford University
Gerlinde Obermoser: Stanford University
Gary M. Shaw: Stanford University
David K. Stevenson: Stanford University
Martin S. Angst: Stanford University
Brice Gaudilliere: Stanford University
Nima Aghaeepour: Stanford University

Nature Communications, 2020, vol. 11, issue 1, 1-9

Abstract: Abstract High-throughput single-cell analysis technologies produce an abundance of data that is critical for profiling the heterogeneity of cellular systems. We introduce VoPo ( https://github.com/stanleyn/VoPo ), a machine learning algorithm for predictive modeling and comprehensive visualization of the heterogeneity captured in large single-cell datasets. In three mass cytometry datasets, with the largest measuring hundreds of millions of cells over hundreds of samples, VoPo defines phenotypically and functionally homogeneous cell populations. VoPo further outperforms state-of-the-art machine learning algorithms in classification tasks, and identified immune-correlates of clinically-relevant parameters.

Date: 2020
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DOI: 10.1038/s41467-020-17569-8

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